Journal
APPLIED ERGONOMICS
Volume 90, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apergo.2020.103262
Keywords
Fatigue forecast; Material Handling; Vector Error Correction Model; Wearable Sensors
Categories
Funding
- American Society of Safety Professionals Foundation
- National Science Foundation, USA [CMMI-1635927]
- Neil R. Anderson Endowed Assistant Professorship at Miami University, USA
- Van Andel Endowed Professorship at Miami University, USA
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This study examined the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion and gait data from wearable sensors. The results indicated that the VECM model, incorporating historical RPE and wearable sensor data, outperformed other models in forecasting three or more time periods ahead.
Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naive method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) < 1.24 and median MAE < 1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker's condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.
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